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Monitoring of Systemic and Hepatic Hemodynamic Parameters in Mice
Published on: October 4, 2014
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Robust estimation of hemo-dynamic parameters in traditional DCE-MRI models
Mikkel B Hansen1, Anna Tietze1,2, Søren Haack3,4
1Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Plos One
|January 4, 2019
Summary
A new Bayesian method improves dynamic contrast-enhanced MRI analysis by accurately estimating tissue hemodynamic parameters. This approach enhances image readability and reduces non-physiological values in pharmacokinetic models for better cancer diagnosis.
Area of Science:
- Medical Imaging
- Biophysics
- Pharmacokinetics
Background:
- Dynamic Contrast-Enhanced (DCE) MRI is crucial for separating perfusion and leakage signals.
- Accurate estimation of pharmacokinetic parameters is essential for robust DCE-MRI analysis.
Purpose of the Study:
- To present and quantify the performance of a Bayesian method for computing tissue hemodynamic parameters from DCE data.
- To compare the Bayesian scheme against the standard Levenberg-Marquardt (LM) algorithm using established pharmacokinetic models.
Main Methods:
- A Bayesian parameter estimation scheme was developed and tested using digital phantoms of the extended Tofts model (ETM) and two-compartment exchange model (2CXM).
- The method was validated by analyzing DCE data from 19 glioma patients, assessing the extra vascular volume (ve) and comparing non-physiological high-intensity values between models.
Main Results:
- The Bayesian scheme outperformed the LM technique in digital phantom simulations.
- Limitations in parameter reliability related to scan duration were identified for the 2CXM.
- The Bayesian method significantly reduced non-physiological high-intensity ve values for both ETM (p<0.0001) and 2CXM (p<0.0001), improving image readability in patient data.
Conclusions:
- The Bayesian parameter estimation scheme offers substantial improvement in the perceptive quality of pharmacokinetic parameters derived from advanced compartment models.
- This method provides a more robust and reliable analysis of DCE-MRI data compared to traditional techniques.
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